Data visualization in R

STAT 4101L, Fall 2023

Surya Lamichhane

By the end of this lesson, you will be able to:

• Visualize data using base R
• Visualize data using ggplot function from ggplot2 package

What is data visualization? (IBM's Definition)

Data visualization is the representation of data through use of common graphics, such as charts, plots, info-graphics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand. This technique mainly use for

• data exploration,
• idea generation,
• idea illustration,
• visual discovery, and
• story telling.

Part 1: Data vizulization using base R

1. Plot function in R

For more details see [https://r-coder.com/plot-r/]

Syntax

plot(x, y, ...)
- the following arguments are optional 
for dot plot: type = 'p' (default)
for line chart: type = 'l'
to assign plot title: main = "title", a charactor field
xlab = "Name of X varaible", a charactor field
ylab = "Name of y varaible", a charactor field
xlim = limit of x values, a numerice range
ylim =  limit of y values, a numerice range

plot(0,1, type = 'n')

Scattor plot

A scatter chart (or a scatter plot) is a chart that shows the relationship between two quantitative variables.

• very powerful techniques to investigate relationship or trend.

str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

x = iris$Sepal.Length
y= iris$Sepal.Width
plot(x, y)

Label the axes and title of the plot

x = iris$Sepal.Length
y= iris$Sepal.Width
plot(x, y, type = 'p', xlim = range(x), ylim = range(y), 
     xlab = "Sepal.Length", 
     ylab = "Sepal.Width", 
     main = "Association of Sepal.Length and Sepal.Width of iris data")

Color of points

For more details see [http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf]

x = iris$Sepal.Length
y= iris$Sepal.Width
plot(x, y, col = 'red')

Plot character in R

For more details see here [https://www.r-bloggers.com/2021/06/r-plot-pch-symbols-different-point-shapes-in-r/]

x = iris$Sepal.Length
y= iris$Sepal.Width
plot(x, y, col = 'red', pch = 19)

• Plot by category

x = iris$Sepal.Length
y= iris$Sepal.Width
plot(x, y, col = iris$Species, pch = 10, 
     main = "Color by Species")

Add another y-variable when your x-varaible is same

x = iris$Sepal.Length
y= iris$Sepal.Width
y1 = iris$Petal.Width
plot(x, y, ylim = range(y, y1), col = 'red', pch = 10)
points(x, y1, col = 'blue', pch = 20)

Combining plots

In the previous example we observed the association of Sepal.Width and Petal.Width with x-variable Sepal.Length. Now let's observe the association of those y-variables with x-variables Sepal.Length and Petal.Length.

It is very easy to combine multiple plots into one overall graph in R, using the par(mfrow = c(i, j)) .

par(mfrow = c(i, j)): combines the plots i indicates number of rows j indicates number of columns

• Find the association of Sepal.Length with other varaibles

par(mfrow = c(2, 1))
#plot 1
x1 = iris$Sepal.Length
y1= iris$Sepal.Width
x2 = iris$Petal.Length
y2 = iris$Petal.Width
plot(x1, y1, xlab = "Sepal.Length",  ylab = "Sepal.Width", col = 'red', pch = 19)
plot(x2, y2, xlab = "Sepal.Length",  ylab = "Petal.Length", col = 'green', pch = 20)

• using a loop

par(mfrow = c(2, 2))
#plot 1
for (j in 2:4){
plot(iris$Sepal.Length, iris[,j], ylim = range(iris[,2:4]), xlab = "Sepal.Length",  
     ylab = names(iris)[j], col = iris$Species, pch = 20)
}

Figure size or window in R

It should be noted that in RStudio the graph will be displayed in the pane layout and figure size can be adjusted in r-chunk by assigning values for fig.width and fig.height.

• New plot window in R can be opened using dev.new such as (width=5, height=4, unit="in")
• For more details see [http://doingbayesiandataanalysis.blogspot.com/2012/01/graphics-plots-in-r-for-macos-linux-and.html]

# Figure size in Rstudio ```{r, fig.width = 4, fig.height = 3} x= rnorm(20) y = 2*x+ 1 plot(x, y) ```

options(repr.plot.width=10, repr.plot.height=5, center = TRUE)
par(mfrow = c(1, 2))
#plot 1
x1 = iris$Sepal.Length
y1= iris$Sepal.Width
y2 = iris$Petal.Width
plot(x1, y1, ylim = range(c(y1, y2)), col = 'red', pch = 18)
points(x1, y2, col = 'blue', pch = 20)
#plot 2
x2= iris$Petal.Length
y3 = iris$Sepal.Width
y4 = iris$Petal.Width
plot(x2, y3, ylim = range(c(y3, y4)), col = 'red', pch = 18)
points(x2, y4, col = 'blue', pch = 20)

?par()

Space between combine figures

We can change the parameters mai, mar, tcl. Type help(par) in R-console for more details.

``` mai: A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches. mar: A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(5, 4, 4, 2) + 0.1. ```

• For more details see [https://datavoreconsulting.com/post/spacing-of-panel-figures-in-r/]

options(repr.plot.width=10, repr.plot.height=6)

par(mfrow = c(2, 2), tcl=-0.01, mai=c(0.5,0.5,0.5,0.5))
#plot 1
x = iris$Sepal.Length
y1= iris$Sepal.Width
y2 = iris$Petal.Width
plot(x, y1, ylim = range(y1),  xlab = "Sepal.Length", 
     ylab = "Sepal.Width", col = "black", pch = 18)
#plot2
plot(x, y2, ylim = range(y2), xlab = "Sepal.Length", 
     ylab = "Petal.Width",  col = 'blue', pch = 18)
#plot 3 and 4
x1 = iris$Petal.Length
y3= iris$Sepal.Width
y4 = iris$Petal.Width
plot(x1, y3, ylim = range(y3), xlab = "Petal.Length", 
     ylab = "Sepal.Width", col = "red", pch = 18)
#plot4
plot(x1, y4, ylim = range(y4),  xlab = "Petal.Length", 
     ylab = "Petal.Width",  col = 'green', pch = 18)

Line chart or trace plot

• Mostly used for time series data.
• Useful to check the association of variables by the observations
• lty stands for line type such as dotted, solid see [https://r-charts.com/base-r/line-types/]

x1 = iris$Sepal.Length
x2 = iris$Petal.Length
idx = 1: length(x1)
plot(idx, x1, type = "l", xlab = "", ylab = "", col = 'red', lty = 1, 
     main = "Sepal.Length vs Petal.Length comarision")
lines(idx, x2, type = "l", xlab = "", ylab = "", lty = 2, col = 'blue')

• Let's adjust the limit so that we can clearly see Petal.Length

x1 = iris$Sepal.Length
x2 = iris$Petal.Length
idx = 1: length(x1)
plot(idx, x1, type = "l", xlab = "", ylab = "", ylim = range(x1, x2), 
     lty = 1, col = 'red', main = "Sepal.Length vs Petal.Length comarision")
lines(idx, x2, type = "l", xlab = "", ylab = "", lty = 2, col = 'blue')

Define legends in R

When we are comparing multiple variables using trace plot or scatter plot, it is vary hard to identify the the visual of related variable. So, assigning legend is important in such of cases.

For more details see [https://r-coder.com/add-legend-r/]

x1 = iris$Sepal.Length
x2 = iris$Petal.Length
idx = 1: length(x1)
plot(idx, x1, type = "l", xlab = "", ylab = "", ylim = range(x1, x2), 
     lty = 1, col = 'red', main = "Sepal.Length vs Petal.Length comarision")
lines(idx, x2, type = "l", xlab = "", ylab = "", lty = 2, col = 'blue')

legend(x = "topleft",          # Position
       legend = c("Sepal.Length", "Petal.Length"),  # Legend texts
       lty = c(1, 2),           # Line types
       col = c('red', 'blue'),           # Line colors
       lwd = 2)                 # Line width

x1 = iris$Sepal.Length
x2 = iris$Petal.Length
idx = 1: length(x1)
plot(idx, x1, type = "l", xlab = "", ylab = "", ylim = range(x1, x2), 
     lty = 1, col = 'red', main = "Sepal.Length vs Petal.Length comarision")
lines(idx, x2, type = "l", xlab = "", ylab = "", lty = 2, col = 'blue')
legend(x = "topright",          # Position
       legend = c("Sepal.Length", "Petal.Length"),  # Legend texts
       inset = c(0, 0),
       lty = c(1, 2),           # Line types
       col = c('red', 'blue'),           # Line colors
       lwd = 2)

Bar plot in R

A bar plot is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to their corresponding values (or count). The bars can be plotted vertically or horizontally.

str(mtcars)

'data.frame':	32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

library(dplyr);
car_counts_by_cyl = mtcars %>%
                    group_by(cyl) %>%
                    summarise(count = n())
car_counts_by_cyl

Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

A tibble: 3 × 2

cyl	count
<dbl>	<int>
4	11
6	7
8	14

values = car_counts_by_cyl$count
cyl =car_counts_by_cyl$cyl
#help(barplot)

# One row, two columns
par(mfrow = c(1, 2))
# Absolute frequency barplot
barplot(height = values, names =cyl, xlab = "cyl",
        main = "Absolute frequency",
        col = rainbow(3))

# Relative frequency barplot
barplot(height = prop.table(values)*100, names =cyl, 
        xlab = "cyl", main = "Relative frequency (%)",
        col = rainbow(3))

Boston311_2023_data =
read.csv("https://data.boston.gov/dataset/8048697b-ad64-4bfc-b090-ee00169f2323/resource/e6013a93-1321-4f2a-bf91-8d8a02f1e62f/download/tmp518q5snq.csv")

• Which neighborhood has maximum number of complaints of type "Parking Enforcement"?

library(stringr)
library(dplyr)
Boston311_2023_data$Parking_Enforcement_status <- str_detect(Boston311_2023_data$case_title, 
                                                             regex("\\bParking Enforcement\\b"))
Parking_Enforcement_by_nbd <- Boston311_2023_data %>%
  group_by(neighborhood) %>%
  summarise(nbd_count_Parking_Enforcement = n()) %>%
  arrange(desc(nbd_count_Parking_Enforcement))
head(Parking_Enforcement_by_nbd, 10)

Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

A tibble: 10 × 2

neighborhood	nbd_count_Parking_Enforcement
<chr>	<int>
Dorchester	36272
Roxbury	21426
South Boston / South Boston Waterfront	18835
Allston / Brighton	18490
East Boston	17862
South End	15265
Jamaica Plain	13728
Downtown / Financial District	11526
Greater Mattapan	11191
Back Bay	10559

top_10_nbd = Parking_Enforcement_by_nbd[1:10, ]
barplot(names = top_10_nbd$neighborhood, height = top_10_nbd$nbd_count_Parking_Enforcement,
 col = rainbow(10), las = 2)
#las = 1, group names printed horizontally
#las = 2, group names printed vertically

par(mar, mgp, las)
par(mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
par sets or adjusts plotting parameters. Here we consider the following three parameters: margin size (mar), axis label locations (mgp), and axis label orientation (las).
mar – A numeric vector of length 4, which sets the margin sizes in the following order: bottom, left, top, and right. The default is c(5.1, 4.1, 4.1, 2.1).
mgp – A numeric vector of length 3, which sets the axis label locations relative to the edge of the inner plot window. The first value represents the location the labels (i.e. xlab and ylab in plot), the second the tick-mark labels, and third the tick marks. The default is c(3, 1, 0).
las – A numeric value indicating the orientation of the tick mark labels and any other text added to a plot after its initialization. The options are as follows: always parallel to the axis (the default, 0), always horizontal (1), always perpendicular to the axis (2), and always vertical (3).

### Horizontal barplot
par(mar = c(4, 16, 2, 2))
top_10_nbd = Parking_Enforcement_by_nbd[1:10, ]
barplot(names = top_10_nbd$neighborhood, height = top_10_nbd$nbd_count_Parking_Enforcement,
 col = rainbow(10), horiz = TRUE, las = 1)

### Barplot for continuous variable
var1 = iris$Sepal.Length
cut_off = c(0, 5, 6, 7 , 8)
catgory = c("low", "low_mid", "high_mid", "high")
Sepal_Len_cat1 = cut(var1, breaks = cut_off, include.lowest = TRUE, right = FALSE, labels = catgory)
iris_new = cbind(iris, Sepal_Len_cat1)
barplot(table(iris_new$Sepal_Len_cat1), col = rainbow(4), 
        legend.text = levels(iris_new$Sepal_Len_cat1))# With Legend

Grouped barplot in R

str(mtcars)

'data.frame':	32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Variable am to factor
am = mtcars$am
am <- factor(am)
# Change factor levels
levels(am) <- c("Automatic", "Manual")
summary_data <- tapply(mtcars$hp, list(cylinders = mtcars$cyl,
                      transmission = am),FUN = mean, na.rm = TRUE)
summary_data

A matrix: 3 × 2 of type dbl

	Automatic	Manual
4	84.66667	81.8750
6	115.25000	131.6667
8	194.16667	299.5000

barplot(summary_data, xlab = "Transmission type",
        main = "Horsepower mean",
        col = rainbow(3),
        beside = TRUE,
        legend.text = rownames(summary_data),
        args.legend = list(title = "Cylinders", x = "topright",
                           inset = c(-0.20, 0)))

Stacked barplot in R

par(mar = c(5, 5, 4, 10), las = 0)
barplot(summary_data,
        main = "Horsepower mean",
        xlab = "Transmission type", ylab = "HP mean",
        col = c('red', 'blue', 'green'),
        legend.text = rownames(summary_data),
        beside = FALSE, # Stacked bars (default)
        args.legend = list(title = "Cylinders", x = "topright",
                           inset = c(-0.2, 0)))

Pie-chart

A pie chart is used to represent data in numerical proportions. Pie chart in R is created using pie() function.

# cyl-wise distribution of data using pie-chart
count_cars <- mtcars %>% 
  group_by(cyl) %>%
  summarise(count = n())

For hcl.colors see ["https://blog.r-project.org/2019/04/01/hcl-based-color-palettes-in-grdevices/"]

car_type <- paste(count_cars$cyl, "cyl")
count <- count_cars$count

# calculating percentage participation
perc <- round(count/sum(count)* 100, 2)

# add frequency or proportion to country names to create labels
labels <- paste(car_type, perc,'%')
pie(count, labels = labels, radius = 1, col = hcl.colors(n = 3, palette = 'Spectral'),
    border = 'gray', main = "Pie chart in R")

Straight line in R

a. horizontal line in R

• Create a empty box and add a horizontal line

# plot function is used to plot
# the data type with "n" is used to remove the plotted dots
# to remove the plotted data
plot(1, type = 'n', # n stands for no points
     xlab = "", #no x label
     ylab = "", #no y label
     xlim = c(0, 5),  # x limit
     ylim = c(0, 5)  # y limit
     )

plot(1, type = 'n', # n stands for no points
     xlab = "", #no x label
     ylab = "", #no y label
     xlim = c(0, 5),  # x limit
     ylim = c(0, 5)  # y limit
     )
abline(h = 2.5, col = "red")

b. vertical line in R

• Create a empty box and add a vertical line

# plot function is used to plot
# the data type with "n" is used to remove the plotted dots
# to remove the plotted data
plot(1, type = "n", xlab = "",
     ylab = "", xlim = c(0, 5), 
     ylim = c(0, 5))
abline(v = 2, col = 'red')

c. Horizontal and vertical line in R

# plot function is used to plot
# the data type with "n" is used to remove the plotted dots
# to remove the plotted data
plot(1, type = "n", xlab = "",
     ylab = "", xlim = c(0, 5), 
     ylim = c(0, 5))
abline(h = 2.5, v = 2, col = 'red')

d. Line with slope and intercept in R

# plot function is used to plot
# the data type with "n" is used to remove the plotted dots
# to remove the plotted data
plot(1, type = "n", xlab = "",
     ylab = "", xlim = c(0, 5), 
     ylim = c(0, 5))
abline(a = 0, # Intercept
       b = 1, col = 'red')  # Slope
abline(a = 5, # Intercept
       b = -1, col = 'blue')  # Slope

Histogram

Histogram is the most widely used graph to represent quantitative (or numerical) data mostly for the continuous in nature.

• This type of graph is useful to make an idea of the shape or distribution of the given data
• It provides an idea of the density of the underlying distribution of the data,
• It is often useful for density estimation,

Syntax

hist(x,....)
hist(x, breaks = "Sturges",
     freq = NULL, probability = !freq,
     include.lowest = TRUE, right = TRUE,
     density = NULL, angle = 45, col = NULL, border = NULL,
     main = paste("Histogram of" , xname),
     xlim = range(breaks), ylim = NULL,
     xlab = xname, ylab,
     axes = TRUE, plot = TRUE, labels = FALSE,
     nclass = NULL, warn.unused = TRUE, …)

Examples (Histogram)

• Histogram with Frequency

hist(iris$Sepal.Length, breaks = 20, col = 'gray', probability = TRUE)

• Histogram with relative frequency

hist(iris$Sepal.Length, breaks = 15, xlab = 'Sepal.Length', 
     ylab = 'Relative Frequency',probability = TRUE, col = 'gray', 
     main = "Histogram of Sepal.Length of Iris data")

• Histogram with two variables

par(mfrow = c(2, 2))
x <- iris$Sepal.Length    # First group
y <- iris$Petal.Length    # Second group

hist(x,  main = "Histogram of Sepal.Length")
hist(y,  main = "Histogram of Petal.Length")

# Combine plot
hist(x, xlim = c(0, 8),ylim = c(0, 50),  main = "Histogram of Two variables")
hist(y, add = TRUE, col = rgb(1, 0, 0, alpha = 1))

• Add kernel density to histogram (non-parametric curve)

par(mfrow = c(1, 2))
x <- iris$Sepal.Length    # First group
y <- iris$Petal.Length    # Second group

hist(x, probability = TRUE,  main = "Histogram of Sepal.Length")
lines(density(x), lwd = 2, col = 'red')
hist(y,  probability = TRUE, main = "Histogram of Petal.Length")
lines(density(y), lwd = 2, col = 'red')

• Add normal density to histogram

x <- iris$Sepal.Length    # First group
y <- iris$Petal.Length    # Second group
hist(x, ylim = c(0, 0.5), probability = TRUE, 
     main = "Histogram of Sepal.Length")
x_val = seq(min(x), max(x), length.out = 100)
f_val = dnorm(x_val, mean = mean(x), sd = sd(x))
lines(x_val, f_val, lwd = 2, col = 'red')

Box plot in R

Box plots (Chambers 1983) are an excellent tools for detecting and illustrating location and variation changes between different groups of data.

• It allows us to summarize the main characteristics of the data such as position, dispersion, skewness
• It is a very useful tool to identify potential data outliers.

boxplot(x, ylab = "Sepal.Length")

• Horzontal box plot

boxplot(x, xlab = "Sepal.Length", horizontal = TRUE)

• Box plot with points

boxplot(x, xlab = "Sepal.Length", horizontal = TRUE)
stripchart(x, method = "jitter", pch = 19, add = TRUE, col = "red")

• When the data have no outliers, two values from end whiskers represents the min and max values, similarly boundaries of the box are Q1 and Q2, and the middle value represents the median.

• Simple but widely used idea for detecting an outlier

IQR = Q3 - Q1
Usual low value, L = Q1 - 1.5*IQR
Usual high value, U = Q3 + 1.5*IQR
Any value outside of the range between L and U considered as outlier

Example of outliers

x <- rnorm(50, 20, 5)
x1 <- c(-4, -7, 0, 50, 55) # add few extreme data points
x <- c(x, x1)
boxplot(x)

Manual boundry lines for outliers

Q1 <- quantile(x, prob = 0.25)
Q3 <- quantile(x, prob = 0.75)
IQR <- Q3 - Q1
L <- Q1 - 1.5*(IQR)
U <- Q3 + 1.5*(IQR)
boxplot(x, horizontal = TRUE, main = "Detection of outlier uising boxpolt ")
abline(v = L, col = 'red')
abline(v = U, col = 'blue')

• Boxplot of multiple varaibles

boxplot(Sepal.Length ~ Species, data = iris, col = rainbow(3), horizontal = FALSE)

Scattor plot with regression line

x = iris$Sepal.Length
y = iris$Petal.Length
plot(x, y, pch = 19, col = "gray52")


# Linear fit
abline(lm(y ~ x), col = "orange", lwd = 3)

# Smooth fit
lines(lowess(x, y), col = "blue", lwd = 3)

# Legend
legend("topleft", legend = c("Linear", "Smooth"),
       lwd = 3, lty = c(1, 1), col = c("orange", "blue"))

Scatterplot matrix in R

It is a pairwise scatter plot, that shows the pairwise association between variables.

#numerical_df <- subset(iris, select = c(Sepal.Length, Sepal.Width,Petal.Length,Petal.Width))
#pairs(numerical_df)
pairs(~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris)

• Pairs plot by category

pairs(~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, col = iris$Species, data = iris)

Part 2. (Data vizulization in R using ggplot)

ggplot2 is one of the most used packages for data visualization in R and it builds plots in layers.

• ggplot2 builds graphs in layers. It divides the plot into three parts:

  • Data: It is the dataframe that contains data to be plotted.
  • Aesthetics: These are the variables mapping to the visual properties of the plot.
  • Geometry: It refers to the type of graph that is used, such as bar graph or histogram

For more details see the link [http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html]

Scatter plot

# install.packages("ggplot2")
library(dplyr)
library(ggplot2)
iris %>%
ggplot() +
  aes(x = Sepal.Length, y = Sepal.Width) + 
  geom_point(size=2, shape=12) 
  #geom_point(aes(size=Sepal.Length)) # size of points varies as values

Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

• Label in the scatter plot

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(aes(colour = Species)) + # Points and color by group
  scale_color_discrete("Type") +  # Change legend title
  xlab("Sepal Length") +              # X-axis label
  ylab("Sepal Width")  +             # Y-axis label
  theme(axis.line = element_line(colour = "black", # Changes the default theme
                                 size = 0.24))

• Title of the plot

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  ggtitle("Scattor plot in R") +
  theme(plot.title = element_text(hjust=0.5)) + # Assign title on center
  geom_point(aes(color = Species)) + # Points and color by group
  #scale_color_discrete("type") +  # Change legend title
  xlab("Sepal.Length") +              # X-axis label
  ylab("Sepal.Width")  +             # Y-axis label
  theme(axis.line = element_line(colour = "red",size = 0.5)) # Changes the default theme (xy-axes)

• Straigh line using abline()

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  geom_abline(intercept = 3, slope =  0 ) +
  ggtitle("Scattor plot in R") +
  theme(plot.title = element_text(hjust=0.5)) + # Assign title on center
  geom_point(aes(colour = Species)) + # Points and color by group
  scale_color_discrete("Species") +  # Change legend title
  xlab("Sepal.Length") +              # X-axis label
  ylab("Sepal.Width")  +             # Y-axis label
  theme(axis.line = element_line(colour = "black", # Changes the default theme
                                 size = 0.01))

• Remove the grids

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  ggtitle("Scattor plot in R") +
  theme(plot.title = element_text(hjust=0.5)) + # Assign title on center
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  #theme_void() + #remove background
  #theme_classic() +#remove background
  geom_point(aes(colour = Species)) + # Points and color by group
  #scale_color_discrete("Species") +  # Change legend title
  xlab("Sepal.Length") +              # X-axis label
  ylab("Sepal.Width") +             # Y-axis label
  theme(axis.line = element_line(colour = "black", # Changes the default theme
                                 size = 0.5))

Line plots in R

# Change the line type
ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_line(linetype = "dashed")

# add points
ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_line(linetype = "solid")+
  geom_point()

# Add labels and plot by category
ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_line(aes(colour = Species))+
  geom_point(aes(colour = Species)) + # Points and color by group
  #scale_color_discrete("Species") +  # Change legend title
  xlab("Sepal.Length") +              # X-axis label
  ylab("Sepal.Width")                 # Y-axis label

Bar plot in R

df = mtcars %>% 
  group_by(cyl)%>% 
  summarise(count = n())
df

A tibble: 3 × 2

cyl	count
<dbl>	<int>
4	11
6	7
8	14

str(mtcars)

'data.frame':	32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Basic barplot
df$cyl = factor(df$cyl)
ggp <- ggplot(data=df, aes(x = cyl, y = count,  fill = cyl) )+
  geom_bar(stat="identity", width=0.7) +
 theme_minimal()
ggp

• Horizontal Barplot

# for the summary x variable need to be a factor variable
ggp <- ggplot(data = df, aes(y = factor(cyl), x = count,  fill = cyl)) +
  geom_bar(stat="identity", width=0.8) +
  theme_minimal()
ggp

# Don't map a variable to x if you use whole data set
ggp <- ggplot(mtcars, aes(y=factor(cyl), fill = factor(cyl)))+
  scale_color_discrete("cyl") +  # Change legend title
  geom_bar() +
  theme_minimal()
ggp

top_10_nbd = Parking_Enforcement_by_nbd[1:10, ] 

ggp <- ggplot(top_10_nbd, aes(y=neighborhood, x = nbd_count_Parking_Enforcement, fill = neighborhood ))+
  geom_bar(stat="identity") +
  scale_colour_manual(name = "neighborhood")+
   xlab("Parking enforcement count by Neighborhood") +  # X-axis label
  ylab("Neighborhood") +                # Y-axis label
  theme_minimal()
ggp

• Order values in plot

top_10_nbd = Parking_Enforcement_by_nbd[1:10, ] 

ggp <- ggplot(top_10_nbd, aes(y=reorder(neighborhood, nbd_count_Parking_Enforcement), x = nbd_count_Parking_Enforcement, fill = neighborhood ))+
  geom_bar(stat="identity") +
  scale_colour_manual(name = "neighborhood")+
  theme_void()
ggp

Sub-divided bar plot and pie-chart

df = mtcars %>% 
  group_by(cyl)%>% 
  summarise(count = n())

df$cyl = as.factor(df$cyl)
# Basic barplot
ggp <- ggplot(data=df, aes(x ='', y = count, fill = cyl)) +
  geom_bar(stat="identity", width=0.7) +
  theme_minimal()
ggp

• Pie chart

ggp <- ggplot(data=df, aes(x = '', y = count, fill = cyl)) +
  geom_bar(stat="identity", width=0.7) +
  coord_polar("y", start=0)
ggp

df$perc = round(df$count/sum(df$count),4) *100
ggp <- ggplot(data=df, aes(x = '', y = perc, fill = cyl)) +
   geom_col() +
  geom_text(aes(label = paste(perc, '%')), color = rep("white", 3),
            position = position_stack(vjust = 0.5)) +
  coord_polar(theta = "y") + 
  theme_void()
ggp

Histogram in R

#?geom_histogram()

# Basic histogram
ggplot(iris, aes(x=Sepal.Length)) + 
  geom_histogram(bins = 20)

# Change the width of bins
ggplot(iris, aes(x=Sepal.Length)) + 
  geom_histogram(binwidth=0.3,bins = 20)

# Change colors
p <-ggplot(iris, aes(x=Sepal.Length)) + 
  geom_histogram(binwidth=0.3,bins = 20, color="black", fill="gray")+
  theme_void()
p

Add mean line and density plot on the histogram

# Add mean line
p + geom_vline(aes(xintercept=mean(Sepal.Length)),
            color="blue", linetype="dashed", size=1)
# Histogram with density plot
ggplot(iris, aes(Sepal.Length)) + 
 geom_histogram(aes(y= ..density..), colour="black", fill="white")+
 geom_density(alpha=0.5, fill="red") + #transparency parameter
theme_minimal()

Warning message:
“Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.”
Warning message:
“The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.”
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Change histogram plot line colors by groups
ggplot(iris, aes(x=Sepal.Length, color=Species)) +
  geom_histogram(fill="gray")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Change histogram plot line colors by groups
ggplot(iris, aes(x=Sepal.Length,fill=Species, color=Species)) +
  geom_histogram(position="identity", alpha=0.5)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

### Use facets grids
stat_summary <- iris %>% 
  group_by(Species) %>% 
  summarise(mean_SepL = mean(Sepal.Length), median_SepL = median(Sepal.Length))

stat_summary <- data.frame(Species = rep(stat_summary$Species, 2), stat = c(stat_summary$mean_SepL, stat_summary$median_SepL), value = rep(c('mean', 'median'), each = 3))

p <- ggplot(iris, aes(x=Sepal.Length))+
  geom_histogram(color="black", fill="steelblue")+
  facet_grid(Species ~ .) +
  geom_vline(data = stat_summary, mapping = aes(xintercept = stat, color = value))
p

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Box plot

# Basic box plot
p <- ggplot(iris, aes(x=Sepal.Length)) + 
  geom_boxplot()
p

# Horizontal box plot
p + coord_flip()

#box plot for multiple category
ggplot(iris, aes(x=Sepal.Length, y=Species)) +
geom_boxplot()

# Notched box plot
ggplot(iris, aes(x=Sepal.Length, y=Species)) + 
  geom_boxplot(notch=TRUE)

# Change outlier, color, shape and size
ggplot(iris, aes(x=Sepal.Length, y=Species)) + 
  geom_boxplot(outlier.colour="red", outlier.shape=8,
                outlier.size=4)

Change box plot line colors

Box plot line colors can be automatically controlled by the level variable :

# Change box plot line colors by groups
p<-ggplot(iris, aes(y=Sepal.Length, x=Species, color = Species)) +
  geom_boxplot()
p

Change box plot fill colors

# Change box plot colors by groups
p<- ggplot(iris, aes(y=Sepal.Length, x=Species,  fill= Species)) +
   geom_boxplot()
p

p + coord_flip()